|
--- |
|
language: "hr" |
|
|
|
tags: |
|
- text-classification |
|
- hate-speech |
|
|
|
widget: |
|
- text: "Potpredsjednik Vlade i ministar branitelja Tomo Medved komentirao je Vladine planove za zakonsku zabranu pozdrava 'za dom spremni'." |
|
--- |
|
# bcms-bertic-frenk-hate |
|
|
|
Text classification model based on [`classla/bcms-bertic`](https://huggingface.co/classla/bcms-bertic) and fine-tuned on the [FRENK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the Croatian subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or acceptable). |
|
|
|
|
|
|
|
|
|
## Fine-tuning hyperparameters |
|
|
|
Fine-tuning was performed with `simpletransformers`. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are: |
|
|
|
```python |
|
|
|
model_args = { |
|
"num_train_epochs": 12, |
|
"learning_rate": 1e-5, |
|
"train_batch_size": 74} |
|
``` |
|
|
|
## Performance |
|
|
|
The same pipeline was run with two other transformer models and `fasttext` for comparison. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed. |
|
|
|
| model | average accuracy | average macro F1 | |
|
|----------------------------|------------------|------------------| |
|
| bcms-bertic-frenk-hate | 0.8313 | 0.8219 | |
|
| EMBEDDIA/crosloengual-bert | 0.8054 | 0.796 | |
|
| xlm-roberta-base | 0.7175 | 0.7049 | |
|
| fasttext | 0.771 | 0.754 | |
|
|
|
|
|
|
|
From recorded accuracies and macro F1 scores p-values were also calculated: |
|
|
|
Comparison with `crosloengual-bert`: |
|
|
|
| test | accuracy p-value | macro F1 p-value | |
|
|----------------|------------------|------------------| |
|
| Wilcoxon | 0.00781 | 0.00781 | |
|
| Mann Whithney | 0.00108 | 0.00108 | |
|
| Student t-test | 2.43e-10 | 1.27e-10 | |
|
|
|
Comparison with `xlm-roberta-base`: |
|
|
|
| test | accuracy p-value | macro F1 p-value | |
|
|----------------|------------------|------------------| |
|
| Wilcoxon | 0.00781 | 0.00781 | |
|
| Mann Whithney | 0.00107 | 0.00108 | |
|
| Student t-test | 4.83e-11 | 5.61e-11 | |
|
|
|
|
|
|
|
## Use examples |
|
|
|
```python |
|
from simpletransformers.classification import ClassificationModel |
|
|
|
model = ClassificationModel( |
|
"bert", "5roop/bcms-bertic-frenk-hate", use_cuda=True, |
|
|
|
|
|
) |
|
|
|
predictions, logit_output = model.predict(['Ne odbacujem da će RH primiti još migranata iz Afganistana, no neće biti novog vala', |
|
"Potpredsjednik Vlade i ministar branitelja Tomo Medved komentirao je Vladine planove za zakonsku zabranu pozdrava 'za dom spremni' "]) |
|
predictions |
|
### Output: |
|
### array([0, 0]) |
|
``` |
|
|
|
## Citation |
|
|
|
If you use the model, please cite the following paper on which the original model is based: |
|
``` |
|
@inproceedings{ljubesic-lauc-2021-bertic, |
|
title = "{BERT}i{\'c} - The Transformer Language Model for {B}osnian, {C}roatian, {M}ontenegrin and {S}erbian", |
|
author = "Ljube{\v{s}}i{\'c}, Nikola and Lauc, Davor", |
|
booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing", |
|
month = apr, |
|
year = "2021", |
|
address = "Kiyv, Ukraine", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://www.aclweb.org/anthology/2021.bsnlp-1.5", |
|
pages = "37--42", |
|
} |
|
``` |
|
|
|
and the dataset used for fine-tuning: |
|
``` |
|
@misc{ljubešić2019frenk, |
|
title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English}, |
|
author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec}, |
|
year={2019}, |
|
eprint={1906.02045}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL}, |
|
url={https://arxiv.org/abs/1906.02045} |
|
} |
|
``` |
|
|
